Assessment of Social Engineering Vulnerabilities in Tanzanian Higher Learning Institutions

Citation Author(s):
Lucas
Mjema
Submitted by:
Lucas Mjema
Last updated:
Wed, 07/24/2024 - 04:33
DOI:
10.21227/h3nb-jx11
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Abstract 

This dataset comprises qualitative and quantitative data collected from a comprehensive study evaluating the prevalence and types of social engineering vulnerabilities within Tanzanian higher learning institutions. The data was gathered through surveys and structured interviews with 395 participants, including students, academic staff, and administrative staff. Key variables include demographic information, awareness levels of social engineering attacks, types of attacks experienced (phishing, smishing, vishing, pretexting, baiting), incident reporting rates, and satisfaction with incident response mechanisms.

The dataset also encompasses participants' cybersecurity practices, their willingness to use a mobile-based application for assessing and reporting social engineering vulnerabilities, and additional measures suggested to enhance cybersecurity within their institutions. The data is valuable for researchers and practitioners aiming to develop user-centric cybersecurity strategies, improve incident management, and design effective training programs tailored to educational environments.

 

The dataset is anonymized to protect the privacy of the participants and is available for further analysis and research to contribute to the ongoing efforts in mitigating social engineering threats in higher education.

Instructions: 

Usage Instructions:

  1. Download the Dataset: Ensure you have access to the dataset file, which is typically provided in CSV or Excel format.

  2. Data Handling:

    • Use appropriate software (e.g., Excel, SPSS, R, Python) to open and analyze the dataset.
    • Ensure data privacy and confidentiality by adhering to ethical guidelines when handling and sharing the data.
  3. Data Analysis:

    • Perform statistical analyses to explore patterns and correlations within the data.
    • Utilize the dataset to develop user-centric cybersecurity strategies and solutions.
  4. Research and Development:

    • Leverage the dataset to design effective training programs and educational materials.
    • Develop and test mobile applications or other tools aimed at mitigating social engineering threats.
  5. Reporting and Sharing:

    • Cite the dataset appropriately in your research publications.
    • Share your findings and developments with the broader cybersecurity community to enhance collaborative efforts.

Contact Information: For any questions or further information regarding the dataset, please contact:

Lucas H. Mjema
Nelson Mandela African Institution of Science and Technology (NM-AIST)
Email: mjemal@nm-aist.ac.tz

Comments

good

Submitted by DevSham Shamim on Mon, 09/09/2024 - 15:40